A RCS model of complex targets for radar performance prediction

Väilä, Minna; Jylhä, Juha; Vaisanen, Ville; Perälä, Henna; Visa, Ari; Harju, Mikko; Virtanen, Kai

doi:10.1109/radar.2017.7944241

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…It has been shown decades ago that the probability density function (approximated by the histogram) of the RCS characterizes the target’s properties; this is the key idea behind the Swerling models (Swerling, 1960). Recently, the histogram of the RCS was again proposed for classification (Vladyslav and Maxim, 2016) and for enhancing the capabilities of air surveillance radars (Vaila et al , 2017). Statistical description of the RCS is combined with cutting-edge learning methods to classify targets in the thorough work (Cai et al , 2021) with which our approaches have a lot in common, yet they apply parametric distributions to describe the RCS, whereas we use histograms as non-parametric RCS descriptions.…”

Section: Introductionmentioning

confidence: 99%

Target classification using radar cross-section statistics of millimeter-wave scattering

Coşkun

Bilicz

2023

COMPEL

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Purpose This paper aims to discuss the classification of targets based on their radar cross-section (RCS). The wavelength, the dimensions of the targets and the distance from the antenna are in the order of 1 mm, 1 m and 10 m, respectively. Design/methodology/approach The near-field RCS is considered, and the physical optics approximation is used for its numerical calculation. To model real scenarios, the authors assume that the incident angle is a random variable within a narrow interval, and repeated observations of the RCS are made for its random realizations. Then, the histogram of the RCS is calculated from the samples. The authors use a nearest neighbor rule to classify conducting plates with different shapes based on their RCS histogram. Findings This setup is considered as a simple model of traffic road sign classification by millimeter-wavelength radar. The performance and limitations of the algorithm are demonstrated through a set of representative numerical examples. Originality/value The proposed method extends the existing tools by using near-field RCS histograms as target features to achieve a classification algorithm.

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